Fmcw radar, method for processing digital signals, and characterization information detection method

ABSTRACT

A method for processing digital signals is provided. In the method, a plurality of digital signals corresponding to radar signals received by a receiving terminal are superposed, so as to reduce noise caused by environmental interference. Therefore, according to an output signal obtained after the superposition, accurate characterization information of a to-be-detected object can be obtained.

CROSS-REFERENCE TO RELATED APPLICATION

This non-provisional application claims priority under 35 U.S.C. §119(a) to Patent Application No. 110109640 filed in Taiwan, R.O.C. onMar. 17, 2021, the entire contents of which are hereby incorporated byreference.

BACKGROUND

Technical Field

The present invention relates to radar signal processing technologies,and in particular, to a frequency modulated continuous wave (FMCW)radar, a method for processing digital signals, and a characterizationinformation detection method.

Related Art

A radar technology can be applied to a large number of outdoor fieldssuch as speed measurement and ranging. However, in an indoor field, alarge number of environmental interference factors cause inaccuratemeasurement results. For example, floors, walls, and cabinets causesignal reflection interference, and object movements such as swaying ofa curtain and fan rotation can also cause signal disturbance.

SUMMARY

In view of this, according to some embodiments, the FMCW radar includesa transmitter and a receiver. The transmitter is configured to transmita plurality of chirp signals. The receiver is configured to receivereflected chirp signals and generate a plurality of digital signalscorresponding to the chirp signals. The receiver then superposes thedigital signals to obtain an output signal, and calculatescharacterization information of a to-be-detected object according to theoutput signal.

According to some embodiments, the receiver superposes the digitalsignals corresponding to the chirp signals in the same frame to obtainthe output signal.

According to some embodiments, the receiver superposes the digitalsignals corresponding to the chirp signals in a plurality of adjacentframes to obtain the output signal.

According to some embodiments, the receiver aligns the digital signalsbefore superposing the digital signals.

According to some embodiments, the transmitter includes at least onetransmitting antenna. The receiver superposes the digital signalscorresponding to the chirp signals from the same transmitting antenna.

According to some embodiments, the receiver includes a plurality ofreceiving antennas. The receiver superposes the digital signalscorresponding to the chirp signals from the same transmitting antennareceived by one of the receiving antennas to obtain an output signal.

According to some embodiments, the receiver includes a plurality ofreceiving antennas. The receiver superposes the digital signalscorresponding to the chirp signals from the same transmitting antennareceived by the receiving antennas to obtain an output signal.

According to some embodiments, the transmitter includes a plurality oftransmitting antennas. The receiver superposes the digital signalscorresponding to the chirp signals from at least two of the transmittingantennas.

According to some embodiments, the receiver includes a plurality ofreceiving antennas. The receiver superposes the digital signalscorresponding to the chirp signals from at least two of the transmittingantennas received by one of the receiving antennas to obtain an outputsignal.

According to some embodiments, the receiver includes a plurality ofreceiving antennas. The receiver superposes the digital signalscorresponding to the chirp signals from at least two of the transmittingantennas received by the receiving antennas to obtain an output signal.

According to some embodiments, the receiver further generatesstatistical information according to the output signal and corrects theoutput signal according to the statistical information.

According to some embodiments, the statistical information is anabsolute sum of squares, an absolute maximum, a standard deviation, or avariance.

According to some embodiments, the receiver determines, according to theoutput signal and a machine learning model, whether the to-be-detectedobject is present in a detection area.

According to some embodiments, when determining that the to-be-detectedobject is present, the receiver calculates the characterizationinformation of the to-be-detected object according to the output signal.

According to some embodiments, a method for processing digital signalsis performed by a processor in a signal processing device and includes:superposing a plurality of digital signals corresponding to a pluralityof chirp signals received by a receiving terminal of a Doppler radar toobtain an output signal; and calculating characterization information ofthe to-be-detected object according to the output signal.

According to some embodiments, the digital signals corresponding to thesuperposed chirp signals are located in the same frame or in a pluralityof adjacent frames.

According to some embodiments, the to-be-superposed digital signals arealigned before the digital signals are superposed.

According to some embodiments, the method for processing digital signalsfurther includes: generating statistical information according to theoutput signal; and correcting the output signal according to thestatistical information.

According to some embodiments, the method for processing digital signalsfurther includes: determining, according to the output signal and amachine learning model, whether the to-be-detected object is present ina detection area.

According to some embodiments, the step of calculating characterizationinformation of the to-be-detected object according to the output signalis performed when it is determined that the to-be-detected object ispresent.

According to some embodiments, the characterization informationdetection method includes: receiving a plurality of digital detectionsignals corresponding to a Doppler radar; performing a frequency domainanalysis on the digital detection signals to obtain a plurality offrequency domain detection signals; generating statistical informationaccording to the frequency domain detection signals; correcting thefrequency domain detection signals according to the statisticalinformation; determining, according to the corrected frequency domaindetection signals and a machine learning model, whether theto-be-detected object is present in a detection area; and calculatingcharacterization information of the to-be-detected object according tothe corrected frequency domain detection signals in response to theto-be- detected object being present.

Based on the above, according to the FMCW radar, the method forprocessing digital signals, and the characterization informationdetection method in some embodiments, the problem of poor signals causedby interference from static and dynamic environments can be solved, andan amount of to-be-processed data can be reduced to accelerate aprocessing speed. According to the FMCW radar, the method for processingdigital signals, and the characterization information detection methodin some embodiments, it can be identified whether the to-be-detectedobject is present, so as to improve processing efficiency and filtererroneous determination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a usage status of an FMCW radaraccording to some embodiments.

FIG. 2 is a schematic block diagram of the FMCW radar according to someembodiments.

FIG. 3 is a schematic diagram illustrating a radar signal.

FIG. 4 is a detailed block diagram of the FMCW radar according to someembodiments.

FIG. 5 is a schematic diagram illustrating the radar signal that istransmitted and received.

FIG. 6 is a schematic diagram of signal processing according to someembodiments.

FIG. 7 is a schematic block diagram of a signal processing deviceaccording to some embodiments.

FIG. 8 is a flowchart of a method for processing digital signalsaccording to an embodiment.

FIG. 9 is a schematic diagram illustrating signal superposition.

FIG. 10 is a schematic diagram illustrating chirp signals.

FIG. 11 is a schematic diagram illustrating receiving the signals inFIG. 10.

FIG. 12 is another schematic diagram illustrating signal superposition.

FIG. 13 is still another schematic diagram illustrating signalsuperposition.

FIG. 14 is a schematic diagram illustrating digital signals withoutbeing superposed and on which distance Fourier transform is performed ina static environment.

FIG. 15 is a schematic diagram illustrating the digital signals that aresuperposed in the manner shown in FIG. 13 and on which distance Fouriertransform is performed in the static environment.

FIG. 16 is a flowchart of a method for processing digital signalsaccording to another embodiment.

FIG. 17 is a flowchart of calculating information of a to-be-detectedobject according to an embodiment.

FIG. 18 is a schematic diagram illustrating generation of statisticalinformation.

FIG. 19A to FIG. 19E are schematic diagrams illustrating frequencydomain signals under different environmental interference.

FIG. 20A to FIG. 20E are schematic diagrams illustrating correctedfrequency domain signals under different environmental interference.

FIG. 21A is a schematic diagram illustrating a frequency domain signalof a to-be-detected object lying on its side.

FIG. 21B is a schematic diagram illustrating a corrected frequencydomain signal of the to- be-detected object lying on its side.

FIG. 22 is a flowchart of calculating information of the to-be-detectedobject according to another embodiment.

FIG. 23 is a histogram illustrating the to-be-detected object being notpresent.

FIG. 24 is a histogram illustrating the to-be-detected object beingpresent.

FIG. 25 is a flowchart of a characterization information detectionmethod according to some embodiments.

DETAILED DESCRIPTION

FIG. 1 is a schematic diagram of a usage status of an FMCW radar 10according to some embodiments. The FMCW radar 10 can detect a detectionarea 40. A detection result may be used to calculate one or moreinformation of a to-be-detected object 50, for example, a distance, adirection, a moving speed, physiological information (such asheartbeats, breathing), and the like. The to-be-detected object 50 maybe, for example, but not limited to a biological body (such as a humanbody).

FIG. 2 is a schematic block diagram of the FMCW radar 10 according tosome embodiments. The FMCW radar 10 includes a transmitter 20 and areceiver 30. The transmitter 20 transmits a radar signal, and thereceiver 30 receives a reflected radar signal.

Referring to FIG. 3, FIG. 3 is a schematic diagram illustrating theradar signal. The upper half shows a change in an amplitude of the radarsignal with time, and the lower half shows a change in a frequency ofthe radar signal with time. The radar signal transmitted by thetransmitter 20 includes a plurality of chirp signals SC. For clarity ofthe drawing, FIG. 3 shows only one chirp signal SC. The chirp signal SCis a linear frequency modulation pulse signal, which is a sine wavewhose frequency increases in a linear manner with time. In someembodiments, a frequency of the chirp signal SC increases in anon-linear manner. For the convenience of description, the linear manneris described in the following. As shown in FIG. 3, within a duration Tc(such as 40 microseconds), the chirp signal SC linearly increases froman initial frequency (such as 77 GHz) to a final frequency (such as 81GHz) according to a slope S. The initial frequency and the finalfrequency may be selected from a millimetre-wave band (that is 30 GHz to300 GHz). A difference between the initial frequency and the finalfrequency is a pulse bandwidth B.

Referring to FIG. 4 and FIG. 5 together, FIG. 4 is a detailed blockdiagram of the FMCW radar 10 according to some embodiments. FIG. 5 is aschematic diagram illustrating the radar signal that is transmitted andreceived. A transmitter 20 includes a transmitting antenna 22 and aradar transmitter 24. The radar transmitter 24 includes a signalsynthesizer for generating a chirp signal Ct and transmitting the chirpsignal via the transmitting antenna 22. The receiver 30 includes areceiving antenna 32, a radar receiver 34, and a processing unit 36. Thereceiving antenna 32 receives a reflected radar signal (a chirp signalCr). The chirp signal Cr may be used as a delayed version of the chirpsignal Ct. The radar receiver 34 includes a mixer, a low-pass filter,and an analog-to-digital converter. The mixer couples the chirp signalCt from the radar transmitter 24 to the received chirp signal Cr, andcan generate two coupled signals with a sum of frequencies of the twochirp signals Ct and Cr and a difference between the frequencies. Thelow-pass filter performs low-pass filtering on the coupled signal toobtain a coupled signal with the difference between the frequencies ofthe two chirp signals Ct and Cr, which is referred to as an“intermediate frequency signal SI”. The analog-to-digital converterconverts the intermediate frequency signal SI to a digital signal forthe processing unit 36 to process the digital signal. The processingunit 36 may be, for example, a central processing unit (CPU), a graphicsprocessing unit (GPU), or other programmable general-purpose or special-purpose microprocessors, a digital signal processor (DSP), aprogrammable controller, an application specific integrated circuit(ASIC), a programmable logic device (PLD), or other similar devices,chips, integrated circuits, and a combination thereof.

In another embodiment of the present disclosure, the FMCW radar 10further includes a transmission module connected to the processing unit36. The transmission module is configured to transmit, to an edge deviceor a cloud server at the other end, a result obtained through digitalsignal processing by the processing unit 36.

In another embodiment of the present disclosure, the processing unit 36of the FMCW radar 10 only partially processes a digital signal from theanalog-to-digital converter, and a result obtained through partialprocessing is transmitted, through the transmission module of the FMCWradar 10, to the edge device or the cloud server at the other end forsubsequent processing and operations of the digital signal.

In another embodiment of the present disclosure, the processing unit 36of the FMCW radar 10 does not perform any processing on the digitalsignal from the analog-to-digital converter, but the digital signal fromthe analog-to-digital converter is directly transmitted, through thetransmission module of the FMCW radar 10, to the edge device or thecloud server at the other end for processing and operations of thedigital signal.

Referring to FIG. 5, a frequency f₀ of the intermediate frequency signalSI may be represented as Equation 1, S is the slope, and τis a delaytime between transmitting and receiving of the radar signal. Therefore,τmay be represented as Equation 2, d is the distance between thetransmitting antenna of the FMCW radar 10 and a to-be-detected object,and c is a speed of light. Equation 3 can be obtained by substitutingEquation 2 into Equation 1. It may be learned from Equation 3 that thefrequency f₀ of the intermediate frequency signal SI contains distanceinformation (that is, a distance between the FMCW radar 10 and theto-be- detected object 50).

f₀ =S·τ  . . . Equation 1

τ=2d/c  . . . Equation 2

f₀ =2Sd/c  . . . Equation 3

FIG. 6 is a schematic diagram of signal processing according to someembodiments. Chirp signals SC are sequentially numbered as C1, C2, C3, .. . ,Cn, where n is a positive integer. A radar receiver 34 converts, todigital signals SD (respectively represented as D1, D2, . . . , Dn,where n is a positive integer), received intermediate frequency signalsSI corresponding to the chirp signals C1-Cn. Values of the digitalsignals SD may be represented as a one-dimensional array (row matrices).The row matrices are arranged in a longitudinal direction in sequence toform a two-dimensional array A1. It may be understood that the digitalsignals SD may also be arranged in a column matrix and arranged insequence in a lateral direction, so that the two-dimensional array canalso be obtained. Value of the two-dimensional array A1 representssignal strength (an amplitude). Index values in the column of the two-dimensional array A1 correspond to an order of the chirp signals SC (thedigital signals SD). Index values in the row of the two-dimensionalarray Al represents time, that is, the row matrix of the two-dimensionalarray Al represents time domain signals.

The processing unit 36 performs fast Fourier transform (FFT)(hereinafter referred to as “distance Fourier transform”) on each of therow matrices of the two-dimensional array A1 (that is, the digitalsignals SD) to obtain a frequency domain signal SF (respectivelyrepresented as F1, F2,. . . , Fn, where n is a positive integer), thatis, the two-dimensional array A2. Therefore, the row matrix of thetwo-dimensional array A2 is equivalent to spectral distribution. Asdescribed above, the frequency of the intermediate frequency signal SIcontains distance information. That is, the index values in the row ofthe two-dimensional array A2 represent distances. The values of thetwo-dimensional array A2 represents strength of frequencies on thespectrum, which can represent strength of radar signals reflected by theFMCW radar 10 at different distances. As shown in FIG. 6, colored boxesin the two- dimensional array A2 represent peak values (that is, thevalues exceed a threshold), which indicates that there is an object at acorresponding distance of the frequency. According to the frequency ofthe peak value, a distance between the FMCW radar 10 and theto-be-detected object 50 can be calculated.

Since an interval between the chirp signals SC is very short (forexample, tens of microseconds), relatively, a position of the sameobject that reflects the chirp signals SC is basically constant.Therefore, each of the frequency domain signals SF has a colored boxcorresponding to the same distance, and a column of colored boxes ispresented. As shown in FIG. 6, in this example, two columns of coloredboxes are presented. Although the frequency domain signals SF have apeak value in the same column, a slight movement change cannot cause anobvious change in the frequency, but a phase composition causes anobvious influence. The index values in the column of the two-dimensionalarray A2 correspond to the order of the frequency domain signals SF (thechirp signals SC), which is a time order. Therefore, the column matricesof the two-dimensional array A2 may be used as time domain signals. Theprocessing unit 36 performs fast Fourier transform (hereinafter referredto as “Doppler Fourier transform”) on the column matrices of thetwo-dimensional array A2, so that phase frequency domain signals SQ(respectively represented as Q1, Q2, . . . , Qm, where m is a positiveinteger) can be respectively obtained, that is, a two-dimensional arrayA3. Therefore, a column matrix of the two-dimensional array A3 isequivalent to phase spectrum distribution. A phase ø₀ of theintermediate frequency signal SI may be represented as Equation 4, andafter substituted into Equation 2, may be represented as Equation 5, andλis a wavelength. According to Equation 5, Equation 6 can be derived,where v is a speed, Δøis a phase difference between two adjacent chirpsignals (Cn-1, Cn), and Δτis a time difference between two adjacentchirp signals SC. It may be learned from Equation 6 that the phase ofthe intermediate frequency signal SI contains movement information (aspeed). Therefore, the index values in the column of the two-dimensionalarray A3 represent speeds. According to the phase frequency domainsignal SQ, a moving speed of the to-be-detected object 50 or a frequencyof a periodic movement can be calculated, and characterizationinformation of the to-be-detected object 50 (such as movementinformation and physiological information (such as a respiratory rateand a heartbeat frequency)) can be obtained. The two-dimensional arrayA3 in FIG. 6 is given by way of example. In two columns, there are twocolored boxes and three colored boxes, which indicates that there are atleast two to-be-detected objects 50 at two different distances from theFMCW radar 10, and the speed (frequency) corresponding to each of thecolored boxes is the characterization information of the correspondingto-be-detected object 50.

$\begin{matrix}{\phi_{0} = {2\pi f_{0}\tau}} & {{Equation}4}\end{matrix}$ $\begin{matrix}{\phi_{0} = \frac{4\pi d}{\lambda}} & {{Equation}5}\end{matrix}$ $\begin{matrix}{v = \frac{\lambda\Delta\phi}{4\pi\Delta t}} & {{Equation}6}\end{matrix}$

In some embodiments, the processing unit 36 may not perform fast Fouriertransform on the entire two-dimensional array A2, but only perform fastFourier transform on the same peak (two column matrices represented ascolored boxes) of the frequency domain signals SF to reduce the numberof operations and save the calculation time.

From the above description, it may be understood that by performingsignal coupling, analog-digital conversion, and digital signalprocessing on the received chirp signals Cr, the distance informationand the characterization information can be obtained. However, in anindoor environment, the radar signal is easily affected by other factorsother than the detection area 40, which may cause erroneousdetermination of information. For example, the signal is subject tostatic environment interference from reflection of a wall or a floor,and the signal is subject to dynamic environment interference fromdisturbance of other moving objects (such as an electric fan and acurtain). Therefore, after the digital signals SD are obtained, andbefore signal processing such as the above distance Fourier transformand Doppler Fourier transform, the following method for processingdigital signals can be performed to solve the problem of poor signalscaused by the environmental interference.

Referring to FIG. 7 and FIG. 8 together, FIG. 7 is a schematic blockdiagram of a signal processing device 60 according to some embodiments.FIG. 8 is a flowchart of a method for processing digital signalsaccording to an embodiment. The signal processing device 60 includes aprocessor 61 and a storage device 62. The storage device 62 is acomputer-readable storage medium for storing a program 63 executed bythe processor 61 to perform the method for processing digital signals.In some embodiments, the signal processing device 60 is a Doppler radar(such as the above FMCW radar 10), and the processor 61 is the aboveprocessing unit 36. In some embodiments, the Doppler radar is acontinuous wave (CW) radar or an ultra- wideband (UWB) radar. In someembodiments, the signal processing device 60 is an edge device or acloud server, that is, after the FMCW radar 10 obtains digital signalsSD, the digital signals SD are to be transmitted to the edge device orthe cloud server and processed by the edge device or the cloud server.

Referring to FIG. 7 to FIG. 9 together, FIG. 9 is a schematic diagramillustrating signal superposition. A receiving terminal of the Dopplerradar (the FMCW radar 10) converts, to digital signals P1-Pn, thereceived intermediate frequency signals SI corresponding to the chirpsignals C1-Cn. In a process S200, the digital signals SP correspondingto the chirp signals SC are superposed to obtain output signals SE (asshown in FIG. 9, which are respectively represented as E1-Ek, where k isa positive integer). An example shown in FIG. 9 is given fordescription. Every two adjacent digital signals SP are superposed toobtain the output signal SE. For example, the digital signals P1 and P2are superposed to obtain the output signal E1, and the digital signalsP3 and P4 are superposed to obtain the output signal E2. Random noisecaused by the environment may be averaged by superposing the signals, sothat a signal-to- noise ratio (SNR) can be increased. In addition, afterthe signals are superposed, a plurality of one-dimensional arrays may bemerged into one one-dimensional array, which can greatly reduce anamount of to-be-processed data to accelerate the processing speed.Although the superposition of two adjacent digital signals SP is givenby way of example, the embodiment of the present invention is notlimited thereto. Alternatively, more than two adjacent digital signalsSP may be superposed.

In some embodiments, the digital signals SP superposed with each othercorrespond to a plurality of chirp signals SC in the same frame. In someother embodiments, the digital signals SP superposed with each othercorrespond to the plurality of chirp signals SC in a plurality ofadjacent frames. The plurality of adjacent frames may be more than twoframes.

Process S300: According to the superposed output signals SE, the abovedistance Fourier transform and Doppler Fourier transform can beperformed, and information such as distance information andcharacterization information (movement information and physiologicalinformation) of the to-be-detected object 50 can be calculated.

In the above example, the transmitter 20 has one transmitting antenna22, and the receiver 30 has one receiving antenna 32. However, in someembodiments, the transmitter 20 may have a plurality of transmittingantennas 22. Similarly, in some embodiments, the receiver 30 may have aplurality of receiving antennas 32.

FIG. 10 is a schematic diagram illustrating chirp signals SC. Forexample, the transmitter 20 has two transmitting antennas 22. The chirpsignals SC are receptively marked as Tx1 and Tx2, which respectivelyrepresent the chirp signals SC transmitted by a first transmittingantenna 22 and a second transmitting antenna 22. The radar signal isdefined to have a plurality of frames M (respectively represented asM1-Mp, where p is a positive integer). Each of the frames M includes aplurality of chirp signals SC. For example, each of the transmittingantennas 22 alternately transmits the chirp signals Tx1 and Tx2 andtransmits four chirp signals SC in total. A period of the frame M maybe, for example, 20 milliseconds. Within the period of each of theframes M, the processor 61 performs the above method for processingdigital signals according to the chirp signals SC in each of the framesM. In other words, the processor 61 superposes the digital signals SPcorresponding to the chirp signals SC in the same frame M to obtain theoutput signal SE, and the process S300 is performed. For example, withinthe period of the frame M1, the method for processing digital signals isperformed once according to the two chirp signals Tx1 and the two chirpsignals Tx2 in the frame M1. Within the period of the frame M2, themethod for processing digital signals is performed once according to thetwo chirp signals Tx1 and the two chirp signals Tx2 in the frame M2.Different methods for signal superposition are described below.

FIG. 11 is a schematic diagram illustrating receiving the signal of FIG.10. For example, a receiver 30 having four receiving antennas Rx1, Rx2,Rx3, and Rx4 receives the radar signal shown in FIG. 10. The chirpsignals Tx1 and Tx2 are received via each of receiving antennas Rx1,Rx2, Rx3, and Rx4 and are converted to digital signals SP by a radarreceiver 34 (only four digital signals P1-P4 in one frame M are used fordescription). A first digital signal P1 corresponds to a first chirpsignal Tx1 transmitted by the first transmitting antenna 22. A seconddigital signal P2 corresponds to a first chirp signal Tx2 transmitted bythe second transmitting antenna 22. A third digital signal P3corresponds to a second chirp signal Tx1 transmitted by the firsttransmitting antenna 22. A fourth digital signal P4 corresponds to asecond chirp signal Tx2 transmitted by the second transmitting antenna22.

FIG. 12 is another schematic diagram illustrating signal superposition.The processor 61 may superpose the digital signals SP corresponding tothe chirp signals SC respectively received by the receiving antennasRx1-Rx4 from the same transmitting antenna 22. For example, the firstdigital signal P1 and the third digital signal P3 both correspond to thechirp signal Tx1 from the first transmitting antenna 22, both of whichare superposed to obtain superposed signals SG (respectively representedas G1-G4). The processor 61 further superposes the signals (G1-G4)corresponding to the receiving antennas Rx1-Rx4 in the superposedsignals SG to obtain an output signal SE. Stated another way, theprocessor 61 superposes the digital signals SP corresponding to thechirp signals SC received by the receiving antennas Rx1-Rx4 from thesame transmitting antenna 22 to obtain the output signal SE. In someembodiments, the processor 61 superposes the digital signals SPcorresponding to the chirp signals SC from other transmitting antennas22 (for example, the chirp signal Tx2 from the second transmittingantenna 22).

In some embodiments, the processor 61 does not superpose the superposedsignals SG corresponding to the receiving antennas Rx1-Rx4, but selectsthe superposed signal G1, G2, G3, or G4 corresponding to one of thereceiving antennas Rx1-Rx4 as the output signal SE. In other words, thedigital signals SP corresponding to the chirp signals SC received by oneof the receiving antennas Rx1-Rx4 from the same transmitting antenna 22are selected to be superposed to obtain the output signal SE.

FIG. 13 is still another schematic diagram illustrating signalsuperposition. In addition to only superposing the digital signals SPcorresponding to the chirp signals SC from the same transmitting antenna22 shown in FIG. 12, the processor 61 may also superpose the digitalsignals SP corresponding to the chirp signals SC from differenttransmitting antennas 22 (that is, at least two transmitting antennas22). For example, although the digital signals P1-P4 are from differenttransmitting antennas 22, the processor 61 superposes the digitalsignals P1- P4 corresponding to the chirp signals SC received by each ofthe receiving antennas Rx1-Rx4 to obtain the superposed signals SG(respectively represented as G1-G4). The processor 61 further superposesthe signals (G1-G4) corresponding to the receiving antennas Rx1-Rx4 inthe superposed signals SG to obtain an output signal SE. Stated anotherway, the processor 61 superposes the digital signals SP corresponding tothe chirp signals SC received by the receiving antennas Rx1 to Rx4 fromdifferent transmitting antennas 22 (that is, at least two transmittingantennas 22) to obtain an output signal SE.

Referring to FIG. 14 and FIG. 15, FIG. 14 is a schematic diagramillustrating digital signals SP without being superposed and on whichdistance Fourier transform is performed in a static environment. FIG. 15is a schematic diagram illustrating the digital signals that aresuperposed in the manner shown in FIG. 13 and on which the distanceFourier transform is performed in the static environment. A horizontalaxis represents the distance information obtained through distanceFourier transform, a longitudinal axis represents signal strength, and avertical axis represents time. It can be seen that after the signals aresuperposed, interference noise of the static environment on the leftside is obviously reduced.

In some embodiments, the processor 61 does not superpose the superposedsignals SG corresponding to the receiving antennas Rx1-Rx4, but selectsthe superposed signal G1, G2, G3, or G4 corresponding to one of thereceiving antennas Rx1-Rx4 as the output signal SE. In other words, thedigital signals SP corresponding to the chirp signals SC received by oneof the receiving antennas Rx1-Rx4 from different transmitting antennas22 (that is, at least two transmitting antennas 22) are superposed toobtain an output signal SE.

In some embodiments, although the chirp signals SC in the same frame Mare superposed for description above, the processor 61 may alsosuperpose the digital signals SP corresponding to the chirp signals SCin two adjacent frames M to obtain the output signal SE. In someembodiments, the processor 61 may also superpose the digital signals SPcorresponding to the chirp signals SC in at least three adjacent framesM to obtain the output signal SE. As described above, the method ofsuperposing the digital signals SP in at least two adjacent frames M maybe superposing the digital signals SP corresponding to the chirp signalsSC from the same transmitting antenna 22, or superposing the digitalsignals SP corresponding to the chirp signals SC from differenttransmitting antennas 22 are superposed. Details are not describedherein again. As described above, one of the superposed signals (thesuperposed signals SG) may be selected as the output signal SE.alternatively, the superposed signals SG are superposed to obtain theoutput signal SE. Details are not described herein again. Since thedigital signals SP corresponding to the chirp signals SC in at least twoadjacent frames M are superposed, the method for processing digitalsignals is performed once for every at least two frames M.

FIG. 16 is a flowchart of a method for processing digital signalsaccording to another embodiment. Different from FIG. 8, before theprocess S200, a process S100 is performed to align the to-be-superposeddigital signals SP. In this way, a phase delay between the digitalsignals SP can be corrected. In some embodiments, if the phase delaybetween the to-be-superposed digital signals SP is within an allowablerange, the process S100 may not be performed. For example, if theprocessor 61 superposes the chirp signals SC in the same frame M, thephase delay between the digital signals is generally within theallowable range, and the process S100 may not be performed. If theprocessor 61 superposes the chirp signals SC in at least two adjacentframes M, and the phase delay between the digital signals exceeds theallowable range, the process S100 is performed before the process S200.

FIG. 17 is a flowchart of calculating information of the to-be-detectedobject according to an embodiment. In addition to the distance Fouriertransform (step S310), the Doppler Fourier transform (step S370), andthe calculation of information of the to-be-detected object 50 (stepS390), the process S300 further includes steps S330 and S350.

Referring to FIG. 17 and FIG. 18 together, FIG. 18 is a schematicdiagram illustrating generation of statistical information. After afrequency domain signal SF is obtained through step S310 (a horizontalaxis represents distance information obtained after distance Fouriertransform, a longitudinal axis represents signal strength, and avertical axis represents time. In case of representation in twodimensions, it is equivalent to the two-dimensional array A2 shown inFIG. 6), step S330 is performed. In step S330, the processor 61 maygenerate statistical information SV according to the frequency domainsignal SF. Specifically, calculation of the statistical information SVis performed on the frequency domain signal SF according to the timeaxis. Through the statistical information SV, signal characteristics maybe analyzed to facilitate distinction between dynamic interference andan object signal source in an environment. The statistical informationSV may be, for example, an absolute sum of squares, an absolute maximum,a standard deviation, or a variance. In detail, the processor 61calculates the statistical information SV for each of the columnmatrices CL of the two- dimensional array A2, that is, counts the signalstrength in a time sequence at each distance. For example, a standarddeviation p is calculated for each of the column matrices CL. As shownin FIG. 18, a distribution curve W depicted according to the standarddeviations p is displayed, which can be used as a mask for thesubsequent step S350 to correct the signal.

In step S350, the processor 61 corrects the signal according to thestatistical information SV. In other words, the frequency domain signalSF is normalized according to the statistical information SV. Forexample, the signals in the time sequence at each distance is multipliedby the corresponding statistical information SV (such as the standarddeviation p), that is, each of the column matrices CL of thetwo-dimensional array A2 is multiplied by the statistical information SV(such as the standard deviation p) corresponding to each of the columnmatrices CL. As shown in FIG. 18, a corrected frequency domain signalSF' is displayed. More accurate information such as distance informationand characterization information (such as movement information andphysiological information) of the to-be- detected object 50 can becalculated according to the corrected frequency domain signal SF′ (stepS390).

Referring to FIG. 19A to FIG. 19E and FIG. 20A to FIG. 20E together,FIG. 19A to FIG. 19E are schematic diagrams illustratingfrequency-domain signals SF under different environmental interference.FIG. 20A to FIG. 20E are schematic diagrams illustrating correctedfrequency domain signals SF' under different environmental interference.FIG. 19A and FIG. 20A show environmental interference caused when a fanrotates but does not oscillate. FIG. 19B and FIG. 20B show environmentalinterference caused when a fan rotates and oscillates. FIG. 19C and FIG.20C show environmental interference caused when a curtain is motionless.FIG. 19D and FIG. 20D show environmental interference caused when acurtain sways. FIG. 19E and FIG. 20E show environmental interferencecaused when a person moves. It can be clearly seen that through theabove signal correction, the dynamic environment interference can beeliminated indeed.

Referring to FIG. 21A and FIG. 21B together, FIG. 21A is a schematicdiagram illustrating the frequency domain signal SF of theto-be-detected object 50 lying on its side. FIG. 21B is a schematicdiagram illustrating a corrected frequency domain signal SF' of the to-be-detected object 50 lying on its side. Through the above signalcorrection, signal interference from reflection of a bed caused bydifferent postures (for example, lying on the side) of the to-be-detected object 50 can also be eliminated, and therefore influencescaused by different sleeping postures can also be effectivelysuppressed.

Referring to FIG. 7 and FIG. 22 together, FIG. 22 is a flowchart ofcalculating information of the to-be-detected object according toanother embodiment. The storage device 62 further stores a machinelearning model 64, which is trained through the above corrected signalsobtained in different situations. The different situations include:under various environmental conditions (for example, the fan rotates,the curtain sways, and the like), the to- be-detected object 50 ispresent in the detection area 40; and under various environmentalconditions (for example, the fan rotates, the curtain sways, and thelike), the to-be-detected object 50 is not present in the detection area40. Different from FIG. 17, FIG. 22 further includes step S360:determine, by using the machine learning model 64 according to thecorrected signal, whether the to-be-detected object 50 is present in thedetection area 40. If the to-be-detected object 50 is present, stepsS370 and S390 continue to be performed to calculate information such asthe distance information, the characterization information (such asmovement information and physiological information) of theto-be-detected object 50; and if not, the process is ended. Through themachine learning technology, features about whether the to-be- detectedobject 50 is present can be learned, so as to determine whether theto-be-detected object 50 is present. In this way, in addition to savingoperation resources when the to-be-detected object 50 is not present,erroneous determination can also be filtered (for example, a case thatthe characterization information is calculated when the to-be-detectedobject 50 is not present can be avoided).

In some embodiments, when the machine learning model 64 is to be trainedand the machine learning model 64 is used for determination, featureextraction processing may be performed on the corrected signal inadvance, and then an extracted feature is input to the machine learningmodel 64. The feature extraction processing may be statistics of ahistogram and calculation of an average value, a standard deviation, avariance, skewness, kurtosis, and the like. The statistics of thehistogram is given by way of example. Values of the two- dimensionalarray A2 of the corrected frequency domain signal SF' can be normalized,and a number of elements in each signal strength interval can be countedaccording to the normalized value. Referring to FIG. 23 and FIG. 24together, histograms of the to-be-detected object 50 being not presentand the to-be-detected object 50 being present are respectivelyillustrated. 10 signal strength intervals are given by way of example.It can be seen that the histograms in the two situations have differentdistributions.

FIG. 25 is a flowchart of a characterization information detectionmethod according to some embodiments. The characterization informationdetection method may be performed by the processor 61 of the abovesignal processing device 60. In step S410, a plurality of digitaldetection signals (that is, the above digital signals SP) correspondingto the Doppler radar are received. In step S420, a frequency domainanalysis is performed on the digital detection signals to obtain aplurality of frequency domain detection signals (that is, the abovefrequency domain signals SF). In step S430, statistical information(that is, the above statistical information SV) is generated accordingto the frequency domain detection signals. In step S440, the frequencydomain detection signal is corrected according to the statisticalinformation SV. Specifically, step S440 is to normalize the frequencydomain detection signals according to the statistical information SV.The statistical information SV is an absolute sum of squares, anabsolute maximum, a standard deviation, or a variance of the frequencydomain detection signals in a statistical period. In step S450, it isdetermined, according to the corrected frequency domain detectionsignals (that is, the above corrected frequency domain signals SF') andthe machine learning model 64, whether the to-be-detected object 50 ispresent in the detection area 40. In step 460, the characterizationinformation of the to-be-detected object 50 is calculated according tothe corrected frequency domain detection signals SF' in response to theto-be- detected object 50 being present. Relevant descriptions of thesteps are described in detail above, and details are not describedherein again.

In some embodiments, the machine learning model 64 is stored in an edgedevice or a cloud server, and the FMCW radar 10 transmits the extractedfeatures to the edge device or the cloud server through the transmissionmodule of the FMCW radar for subsequent machine model training ordetection and determination. Alternatively, the FMCW radar 10 transmitsthe above corrected signals to the edge device or the cloud serverthrough the transmission module of the FMCW radar for subsequent digitalsignal processing, machine model training, and/or detection anddetermination.

Based on the above, according to the FMCW radar and method forprocessing digital signals in some embodiments, the problem of poorsignals caused by interference from static and dynamic environments canbe solved, and an amount of to-be-processed data can be reduced toaccelerate a processing speed. According to the FMCW radar and themethod for processing digital signals in some embodiments, it can beidentified whether the to-be-detected object 50 is present, so as toimprove processing efficiency and filter erroneous determination.

What is claimed is:
 1. A frequency modulated continuous wave (FMCW)radar, comprising: a transmitter configured to transmit a plurality ofchirp signals; and a receiver configured to receive reflected chirpsignals, generate a plurality of digital signals corresponding to thechirp signals, superpose the digital signals to obtain an output signal,and calculate characterization information of a to-be-detected objectaccording to the output signal.
 2. The FMCW radar according to claim 1,wherein the receiver superposes the digital signals corresponding to thechirp signals in the same frame to obtain the output signal.
 3. The FMCWradar according to claim 1, wherein the receiver superposes the digitalsignals corresponding to the chirp signals in a plurality of adjacentframes to obtain the output signal.
 4. The FMCW radar according to claim3, wherein the receiver aligns the digital signals before superposingthe digital signals.
 5. The FMCW radar according to claim 1, wherein thetransmitter comprises at least one transmitting antenna, and thereceiver superposes the digital signals corresponding to the chirpsignals from the same transmitting antenna.
 6. The FMCW radar accordingto claim 5, wherein the receiver comprises a plurality of receivingantennas, and the receiver superposes the digital signals correspondingto the chirp signals received by one of the receiving antennas from thesame transmitting antenna to obtain the output signal.
 7. The FMCW radaraccording to claim 5, wherein the receiver comprises a plurality ofreceiving antennas, and the receiver superposes the digital signalscorresponding to the chirp signals received by the receiving antennasfrom the same transmitting antenna to obtain the output signal.
 8. TheFMCW radar according to claim 1, wherein the transmitter comprises aplurality of transmitting antennas, and the receiver superposes thedigital signals corresponding to the chirp signals from at least two ofthe transmitting antennas.
 9. The FMCW radar according to claim 8,wherein the receiver comprises a plurality of receiving antennas, andthe receiver superposes the digital signals corresponding to the chirpsignals received by one of the receiving antennas from at least two ofthe transmitting antennas to obtain the output signal.
 10. The FMCWradar according to claim 8, wherein the receiver comprises a pluralityof receiving antennas, and the receiver superposes the digital signalscorresponding to the chirp signals received by the receiving antennasfrom at least two of the transmitting antennas to obtain the outputsignal.
 11. The FMCW radar according to claim 1, wherein the receiverfurther generates statistical information according to the output signaland corrects the output signal according to the statistical information.12. The FMCW radar according to claim 11, wherein the statisticalinformation is an absolute sum of squares, an absolute maximum, astandard deviation, or a variance.
 13. The FMCW radar according to claim11, wherein the receiver determines, according to the output signal anda machine learning model, whether the to-be-detected object is presentin a detection area.
 14. The FMCW radar according to claim 13, whereinwhen determining that the to-be- detected object is present, thereceiver calculates the characterization information of the to-be-detected object according to the output signal.
 15. A method forprocessing digital signals, performed by a processor in a signalprocessing device and comprising: superposing a plurality of digitalsignals corresponding to a plurality of chirp signals received by areceiving terminal of a Doppler radar to obtain an output signal; andcalculating characterization information of a to-be-detected objectaccording to the output signal.
 16. The method for processing digitalsignals according to claim 15, wherein the digital signals correspondingto the superposed chirp signals are located in the same frame or in aplurality of adjacent frames.
 17. The method for processing digitalsignals according to claim 15, wherein before the step of superposingthe digital signals corresponding to the chirp signals received by thereceiving terminal of the Doppler radar to obtain the output signal, themethod further comprises: aligning the to-be-superposed digital signals.18. The method for processing digital signals according to claim 15,further comprising: generating statistical information according to theoutput signal; and correcting the output signal according to thestatistical information.
 19. The method for processing digital signalsaccording to claim 15, further comprising: determining, according to theoutput signal and a machine learning model, whether the to-be-detectedobject is present in a detection area.
 20. The method for processingdigital signals according to claim 18, further comprising: determining,according to the output signal and a machine learning model, whether theto-be-detected object is present in a detection area.
 21. The method forprocessing digital signals according to claim 20, wherein the step ofcalculating the characterization information of the to-be-detectedobject according to the output signal is performed when it is determinedthat the to-be-detected object is present.
 22. A characterizationinformation detection method, comprising: receiving a plurality ofdigital detection signals corresponding to a Doppler radar; performing afrequency domain analysis on the digital detection signals to obtain aplurality of frequency domain detection signals; generating statisticalinformation according to the frequency domain detection signals;correcting the frequency domain detection signals according to thestatistical information; determining, according to the correctedfrequency domain detection signals and a machine learning model, whethera to-be-detected object is present in a detection area; and calculatingcharacterization information of the to-be-detected object according tothe corrected frequency domain detection signals in response to theto-be-detected object being present.
 23. The characterizationinformation detection method according to claim 22, wherein thecorrecting the frequency domain detection signals according to thestatistical information further comprises: normalizing the frequencydomain detection signals according to the statistical information,wherein the statistical information is an absolute sum of squares, anabsolute maximum, a standard deviation, or a variance of the frequencydomain detection signals in a statistical period.